SCD‐<i>plus</i> features and AD biomarkers in cognitively unimpaired samples: A meta‐analytic approach for nine cohort studies
Bibliographic record
Abstract
INTRODUCTION: Specific features of subjective cognitive decline (SCD-plus) have been proposed to indicate an increased risk of preclinical Alzheimer's disease (AD). However, few studies have examined how these features relate to AD biomarkers in cognitively unimpaired (CU) older adults. METHODS: Meta-analyses were performed using cross-sectional data from nine cohorts (n = 7219, mean age (SD): 71.17 (5.9), 56.5% female) to determine associations of SCD-plus features with positron emission tomography (PET)- or cerebrospinal fluid (CSF)-derived amyloid beta (Aβ) and tau biomarkers. RESULTS: Participants with preclinical AD (community-based only) were more likely to fulfill SCD-plus features. The presence of self-reported memory decline, associated concern/worry, and a higher number of fulfilled features were all associated with high Aβ levels. Only the latter was associated with abnormal tau. DISCUSSION: Simultaneous endorsement of multiple SCD-plus features is a robust indicator of abnormal AD biomarkers in CU older adults, whereas isolated SCD features seem only sensitive to elevated Aβ, supporting their value as early behavioral markers of preclinical AD. HIGHLIGHTS: About two-tenths of our sample had abnormal amyloid beta (Aβ) levels with evidence of subjective cognitive decline (SCD). Preclinical AD subsamples (community-based) had a higher percentage of participants meeting SCD-plus features. Self-reported memory decline and concern/worry were the sole features associated with high Aβ, but not tau, burden. A higher number of fulfilled SCD-plus features are linked to high Aβ and tau burden. Use of multiple SCD-plus features may help identify early stages of biological AD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.058 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".